class_docstr: The prediction error visualizer plots the actual targets from the dataset
  against the predicted values generated by our model(s). This visualizer is
  used to detect noise or heteroscedasticity along a range of the target
  domain.
  
  Parameters
  ----------
  
  estimator : a Scikit-Learn regressor
      Should be an instance of a regressor, otherwise will raise a
      YellowbrickTypeError exception on instantiation.
      If the estimator is not fitted, it is fit when the visualizer is fitted,
      unless otherwise specified by ``is_fitted``.
  
  ax : matplotlib Axes, default: None
      The axes to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  shared_limits : bool, default: True
      If shared_limits is True, the range of the X and Y axis limits will
      be identical, creating a square graphic with a true 45 degree line.
      In this form, it is easier to diagnose under- or over- prediction,
      though the figure will become more sparse. To localize points, set
      shared_limits to False, but note that this will distort the figure
      and should be accounted for during analysis.
  
  bestfit : bool, default: True
      Draw a linear best fit line to estimate the correlation between the
      predicted and measured value of the target variable. The color of
      the bestfit line is determined by the ``line_color`` argument.
  
  identity : bool, default: True
      Draw the 45 degree identity line, y=x in order to better show the
      relationship or pattern of the residuals. E.g. to estimate if the
      model is over- or under- estimating the given values. The color of the
      identity line is a muted version of the ``line_color`` argument.
  
  alpha : float, default: 0.75
      Specify a transparency where 1 is completely opaque and 0 is completely
      transparent. This property makes densely clustered points more visible.
  
  is_fitted : bool or str, default='auto'
      Specify if the wrapped estimator is already fitted. If False, the estimator
      will be fit when the visualizer is fit, otherwise, the estimator will not be
      modified. If 'auto' (default), a helper method will check if the estimator
      is fitted before fitting it again.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  
  score_ : float
      The R^2 score that specifies the goodness of fit of the underlying
      regression model to the test data.
  
  Examples
  --------
  
  >>> from yellowbrick.regressor import PredictionError
  >>> from sklearn.linear_model import Lasso
  >>> model = PredictionError(Lasso())
  >>> model.fit(X_train, y_train)
  >>> model.score(X_test, y_test)
  >>> model.show()
  
  Notes
  -----
  
  PredictionError is a ScoreVisualizer, meaning that it wraps a model and
  its primary entry point is the `score()` method.
docstr: The score function is the hook for visual interaction. Pass in test
  data and the visualizer will create predictions on the data and
  evaluate them with respect to the test values. The evaluation will
  then be passed to draw() and the result of the estimator score will
  be returned.
  
  Parameters
  ----------
  X : array-like
      X (also X_test) are the dependent variables of test set to predict
  
  y : array-like
      y (also y_test) is the independent actual variables to score against
  
  Returns
  -------
  score : float
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code: def score(self, X, y, **kwargs):
      """
      The score function is the hook for visual interaction. Pass in test
      data and the visualizer will create predictions on the data and
      evaluate them with respect to the test values. The evaluation will
      then be passed to draw() and the result of the estimator score will
      be returned.
  
      Parameters
      ----------
      X : array-like
          X (also X_test) are the dependent variables of test set to predict
  
      y : array-like
          y (also y_test) is the independent actual variables to score against
  
      Returns
      -------
      score : float
      """
      # super will set score_ on the visualizer
      super(PredictionError, self).score(X, y, **kwargs)
  
      y_pred = self.predict(X)
      self.draw(y, y_pred)
  
      return self.score_
  
file_path: yellowbrick/yellowbrick/regressor/prediction_error.py
imports: from yellowbrick.style.palettes import LINE_COLOR
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.bestfit import draw_best_fit, draw_identity_line
  from yellowbrick.regressor.base import RegressionScoreVisualizer
label_desc: Use image node_id 2 for calling the PredictionError obj's underlying member method code with example usage: obj.score(X, y) and returns: self